Universal model for UV/Vis spectroscopy of gold nanoparticles

Chemistry
Nanotechnology
Published

July 9, 2025

Machine learning models have limited interoperability in analytical chemistry; mostly.

Experiments always vary between labs. This can be enough to disturb machine learning models trained on small data.

The list goes on and on.

Here is a beautiful example of a β€œuniversal machine learning model” Gleason et al. (2024) for UV/Vis spectroscopy of gold nanoparticles. A method that almost always works!

From spectrum to population from Gleason et al. (2024)

Gold Nanorod shape distribution prediction

The model is trained on simulated UV/Vis spectra of gold nanorods with different aspect ratios, and it can predict the shape distribution from the spectrum.

The method is called AuNR-SMA and outlined in the paper β€œAutomated Gold Nanorod Spectral Morphology Analysis Pipeline”. It is a computational method, but not machine learning.

Computational workflow of AuNR-SMA Gleason et al. (2024)

The raw material is a bag of simulated spectra for gold nanorods (AuNRs) with varying aspect ratios and dimensions. The authors assume that the particles dimensions follow a Gaussian distribution. The observed spectrum is then reconstructed sum of spectra of individual particles. There is no learning involved, just physics and math.

βœ… Interoperability Comparison: AuNR-SMA vs Traditional Methods

Feature AuNR-SMA Traditional TEM Empirical UV-Vis Methods
πŸ”¬ Equipment Required βœ… Any UV-Vis-NIR spectrophotometer ❌ Expensive TEM facility ⚠️ Specific calibrated instrument
🌍 Cross-Lab Transfer βœ… Physics-based = Universal βœ… Direct imaging ❌ Requires recalibration
⏱️ Analysis Time βœ… <5 minutes ❌ Hours + sample prep βœ… Minutes
πŸ’° Cost per Sample βœ… <$1 ❌ $50-200 βœ… <$1
πŸ“Š Information Obtained βœ… Full size distributions βœ… Individual particles ⚠️ Only average values
πŸ€– Automation Ready βœ… Fully automated ⚠️ Limited ⚠️ Instrument-specific
πŸ‘©β€πŸ”¬ Expertise Needed βœ… Minimal training ❌ Specialized training ⚠️ Method development
πŸ“ˆ Throughput βœ… 100s/day ❌ 10s/day βœ… 100s/day

🎯 What Makes AuNR-SMA Universally Transferable

Enabler

The paper mentions explores three applications.

  1. The authors automate the analysis of one-pot seedless high throughput AuNR synthesis.
  2. Then they train machine learning models to predict AuNR synthesis outcomes
  3. They use spectra from the literature to infer population level data that was not reported

Results prediciton systehesis distribution with machine learning Gleason et al. (2024)

Any type of lab can benefit from this method.

Lab Type How AuNR-SMA Helps
🏭 Industrial Scale-Up Monitor batch-to-batch consistency without TEM delays
πŸ”¬ Academic Research Publish complete size distributions, not just TEM samples
πŸ₯ Biomedical Applications Rapid QC for therapeutic nanoparticles
🀝 Collaborative Projects Same analysis method across all partner labs
πŸ“– Literature Review Extract quantitative data from published spectra

References

Gleason, Samuel P., Jakob C. Dahl, Mahmoud Elzouka, Xingzhi Wang, Dana O. Byrne, Hannah Cho, Mumtaz Gababa, et al. 2024. β€œAutomated Gold Nanorod Spectral Morphology Analysis Pipeline.” ACS Nano 18 (51): 34646–55. https://doi.org/10.1021/acsnano.4c09753.